Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations58386
Missing cells10999
Missing cells (%)1.2%
Duplicate rows25
Duplicate rows (%)< 0.1%
Total size in memory4.0 MiB
Average record size in memory72.0 B

Variable types

Numeric8
Categorical8

Alerts

Dataset has 25 (< 0.1%) duplicate rowsDuplicates
label_1 is highly overall correlated with pred_1High correlation
label_5 is highly overall correlated with pred_5High correlation
pred_0 is highly overall correlated with pred_1 and 3 other fieldsHigh correlation
pred_1 is highly overall correlated with label_1 and 4 other fieldsHigh correlation
pred_2 is highly overall correlated with pred_0 and 3 other fieldsHigh correlation
pred_4 is highly overall correlated with pred_0 and 3 other fieldsHigh correlation
pred_5 is highly overall correlated with label_5 and 4 other fieldsHigh correlation
race is highly imbalanced (52.5%)Imbalance
label_2 is highly imbalanced (59.8%)Imbalance
label_3 is highly imbalanced (85.7%)Imbalance
race has 8160 (14.0%) missing valuesMissing
gender has 2839 (4.9%) missing valuesMissing

Reproduction

Analysis started2024-08-16 14:19:16.175507
Analysis finished2024-08-16 14:19:27.227146
Duration11.05 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

subject_id
Real number (ℝ)

Distinct12507
Distinct (%)21.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15047124
Minimum10001122
Maximum19998444
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.3 KiB
2024-08-16T16:19:27.318635image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum10001122
5-th percentile10578325
Q112601466
median15108002
Q317461126
95-th percentile19453522
Maximum19998444
Range9997322
Interquartile range (IQR)4859660.2

Descriptive statistics

Standard deviation2842240.5
Coefficient of variation (CV)0.18888928
Kurtosis-1.1777726
Mean15047124
Median Absolute Deviation (MAD)2434888
Skewness-0.029466504
Sum8.785414 × 1011
Variance8.078331 × 1012
MonotonicityNot monotonic
2024-08-16T16:19:27.469778image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16662316 159
 
0.3%
18001923 100
 
0.2%
18902344 98
 
0.2%
11021643 98
 
0.2%
11648387 81
 
0.1%
16924675 80
 
0.1%
14508231 76
 
0.1%
15656571 74
 
0.1%
10578325 68
 
0.1%
15131736 67
 
0.1%
Other values (12497) 57485
98.5%
ValueCountFrequency (%)
10001122 5
 
< 0.1%
10001884 51
0.1%
10002013 14
 
< 0.1%
10002430 9
 
< 0.1%
10003255 2
 
< 0.1%
10004720 2
 
< 0.1%
10005001 2
 
< 0.1%
10006023 2
 
< 0.1%
10006501 2
 
< 0.1%
10008064 2
 
< 0.1%
ValueCountFrequency (%)
19998444 2
< 0.1%
19995320 3
< 0.1%
19995258 3
< 0.1%
19995179 1
 
< 0.1%
19994588 4
< 0.1%
19994233 3
< 0.1%
19991424 2
< 0.1%
19991085 1
 
< 0.1%
19990545 2
< 0.1%
19990078 4
< 0.1%

age
Real number (ℝ)

Distinct74
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.011184
Minimum18
Maximum255
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.1 KiB
2024-08-16T16:19:27.608376image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile26
Q149
median62
Q375
95-th percentile91
Maximum255
Range237
Interquartile range (IQR)26

Descriptive statistics

Standard deviation45.523478
Coefficient of variation (CV)0.65965363
Kurtosis10.660975
Mean69.011184
Median Absolute Deviation (MAD)13
Skewness3.1955872
Sum4029287
Variance2072.387
MonotonicityNot monotonic
2024-08-16T16:19:27.747447image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
255 2839
 
4.9%
91 1484
 
2.5%
62 1356
 
2.3%
63 1325
 
2.3%
52 1296
 
2.2%
72 1248
 
2.1%
59 1236
 
2.1%
64 1235
 
2.1%
57 1229
 
2.1%
54 1219
 
2.1%
Other values (64) 43919
75.2%
ValueCountFrequency (%)
18 212
0.4%
19 311
0.5%
20 409
0.7%
21 291
0.5%
22 355
0.6%
23 421
0.7%
24 325
0.6%
25 359
0.6%
26 390
0.7%
27 383
0.7%
ValueCountFrequency (%)
255 2839
4.9%
91 1484
2.5%
89 265
 
0.5%
88 538
 
0.9%
87 512
 
0.9%
86 540
 
0.9%
85 685
 
1.2%
84 695
 
1.2%
83 707
 
1.2%
82 724
 
1.2%

race
Categorical

IMBALANCE  MISSING 

Distinct33
Distinct (%)0.1%
Missing8160
Missing (%)14.0%
Memory size58.5 KiB
WHITE
29729 
BLACK/AFRICAN AMERICAN
8413 
OTHER
 
1638
HISPANIC/LATINO - PUERTO RICAN
 
1164
WHITE - OTHER EUROPEAN
 
1156
Other values (28)
8126 

Length

Max length41
Median length5
Mean length10.827619
Min length5

Characters and Unicode

Total characters543828
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWHITE
2nd rowWHITE
3rd rowWHITE
4th rowWHITE
5th rowBLACK/AFRICAN AMERICAN

Common Values

ValueCountFrequency (%)
WHITE 29729
50.9%
BLACK/AFRICAN AMERICAN 8413
 
14.4%
OTHER 1638
 
2.8%
HISPANIC/LATINO - PUERTO RICAN 1164
 
2.0%
WHITE - OTHER EUROPEAN 1156
 
2.0%
WHITE - RUSSIAN 923
 
1.6%
HISPANIC OR LATINO 815
 
1.4%
UNKNOWN 789
 
1.4%
ASIAN - CHINESE 701
 
1.2%
BLACK/CAPE VERDEAN 683
 
1.2%
Other values (23) 4215
 
7.2%
(Missing) 8160
 
14.0%

Length

2024-08-16T16:19:27.892761image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
white 32120
41.4%
black/african 8757
 
11.3%
american 8735
 
11.2%
6012
 
7.7%
other 2834
 
3.6%
hispanic/latino 2520
 
3.2%
asian 2054
 
2.6%
european 1326
 
1.7%
rican 1164
 
1.5%
puerto 1164
 
1.5%
Other values (38) 10986
 
14.1%

Most occurring characters

ValueCountFrequency (%)
I 68629
12.6%
A 66207
12.2%
E 53758
9.9%
T 41519
 
7.6%
H 39438
 
7.3%
N 38150
 
7.0%
C 35052
 
6.4%
W 33037
 
6.1%
R 27940
 
5.1%
27446
 
5.0%
Other values (17) 112652
20.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 543828
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 68629
12.6%
A 66207
12.2%
E 53758
9.9%
T 41519
 
7.6%
H 39438
 
7.3%
N 38150
 
7.0%
C 35052
 
6.4%
W 33037
 
6.1%
R 27940
 
5.1%
27446
 
5.0%
Other values (17) 112652
20.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 543828
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 68629
12.6%
A 66207
12.2%
E 53758
9.9%
T 41519
 
7.6%
H 39438
 
7.3%
N 38150
 
7.0%
C 35052
 
6.4%
W 33037
 
6.1%
R 27940
 
5.1%
27446
 
5.0%
Other values (17) 112652
20.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 543828
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 68629
12.6%
A 66207
12.2%
E 53758
9.9%
T 41519
 
7.6%
H 39438
 
7.3%
N 38150
 
7.0%
C 35052
 
6.4%
W 33037
 
6.1%
R 27940
 
5.1%
27446
 
5.0%
Other values (17) 112652
20.7%

gender
Categorical

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing2839
Missing (%)4.9%
Memory size456.3 KiB
M
28144 
F
27403 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters55547
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowF
3rd rowF
4th rowF
5th rowF

Common Values

ValueCountFrequency (%)
M 28144
48.2%
F 27403
46.9%
(Missing) 2839
 
4.9%

Length

2024-08-16T16:19:28.018912image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-16T16:19:28.316998image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
m 28144
50.7%
f 27403
49.3%

Most occurring characters

ValueCountFrequency (%)
M 28144
50.7%
F 27403
49.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 55547
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 28144
50.7%
F 27403
49.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 55547
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 28144
50.7%
F 27403
49.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 55547
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 28144
50.7%
F 27403
49.3%

pred_0
Real number (ℝ)

HIGH CORRELATION 

Distinct58297
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.22864699
Minimum0.0007134835
Maximum0.93613154
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.3 KiB
2024-08-16T16:19:28.439116image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0.0007134835
5-th percentile0.019512462
Q10.076932167
median0.18095325
Q30.34323808
95-th percentile0.59315564
Maximum0.93613154
Range0.93541806
Interquartile range (IQR)0.26630592

Descriptive statistics

Standard deviation0.18267495
Coefficient of variation (CV)0.79893878
Kurtosis0.10155325
Mean0.22864699
Median Absolute Deviation (MAD)0.1209184
Skewness0.90499551
Sum13349.783
Variance0.033370136
MonotonicityNot monotonic
2024-08-16T16:19:28.572024image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.11780363 2
 
< 0.1%
0.24564247 2
 
< 0.1%
0.23545875 2
 
< 0.1%
0.11404432 2
 
< 0.1%
0.08073465 2
 
< 0.1%
0.28054523 2
 
< 0.1%
0.19426298 2
 
< 0.1%
0.17781691 2
 
< 0.1%
0.2347063 2
 
< 0.1%
0.4517402 2
 
< 0.1%
Other values (58287) 58366
> 99.9%
ValueCountFrequency (%)
0.0007134835 1
< 0.1%
0.00071961153 1
< 0.1%
0.00081179786 1
< 0.1%
0.00089570356 1
< 0.1%
0.00090980896 1
< 0.1%
0.0009669827 1
< 0.1%
0.0009757418 1
< 0.1%
0.0009865833 1
< 0.1%
0.0011006723 1
< 0.1%
0.0012743742 1
< 0.1%
ValueCountFrequency (%)
0.93613154 1
< 0.1%
0.93382 1
< 0.1%
0.9289067 1
< 0.1%
0.9256189 1
< 0.1%
0.9232685 1
< 0.1%
0.92095166 1
< 0.1%
0.9200051 1
< 0.1%
0.9191345 1
< 0.1%
0.90677124 1
< 0.1%
0.9062864 1
< 0.1%

label_0
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size456.3 KiB
0
51003 
1
7383 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters58386
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 51003
87.4%
1 7383
 
12.6%

Length

2024-08-16T16:19:28.705868image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-16T16:19:28.803812image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 51003
87.4%
1 7383
 
12.6%

Most occurring characters

ValueCountFrequency (%)
0 51003
87.4%
1 7383
 
12.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58386
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 51003
87.4%
1 7383
 
12.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58386
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 51003
87.4%
1 7383
 
12.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58386
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 51003
87.4%
1 7383
 
12.6%

pred_1
Real number (ℝ)

HIGH CORRELATION 

Distinct58285
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.14381424
Minimum4.4961896 × 10-5
Maximum0.99647397
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.3 KiB
2024-08-16T16:19:28.926857image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum4.4961896 × 10-5
5-th percentile0.0019736528
Q10.011122281
median0.044808384
Q30.1856508
95-th percentile0.64826539
Maximum0.99647397
Range0.99642901
Interquartile range (IQR)0.17452852

Descriptive statistics

Standard deviation0.20828639
Coefficient of variation (CV)1.4483016
Kurtosis3.0725192
Mean0.14381424
Median Absolute Deviation (MAD)0.040527415
Skewness1.926109
Sum8396.7382
Variance0.043383221
MonotonicityNot monotonic
2024-08-16T16:19:29.074014image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.056017026 2
 
< 0.1%
0.06861152 2
 
< 0.1%
0.07065508 2
 
< 0.1%
0.061210033 2
 
< 0.1%
0.0425446 2
 
< 0.1%
0.46214718 2
 
< 0.1%
0.031990286 2
 
< 0.1%
0.019157823 2
 
< 0.1%
0.013577934 2
 
< 0.1%
0.31736004 2
 
< 0.1%
Other values (58275) 58366
> 99.9%
ValueCountFrequency (%)
4.4961896 × 10-51
< 0.1%
4.5468667 × 10-51
< 0.1%
5.226288 × 10-51
< 0.1%
5.640238 × 10-51
< 0.1%
7.616898 × 10-51
< 0.1%
7.8047895 × 10-51
< 0.1%
8.106019 × 10-51
< 0.1%
8.346491 × 10-51
< 0.1%
8.3913335 × 10-51
< 0.1%
8.4583764 × 10-51
< 0.1%
ValueCountFrequency (%)
0.99647397 1
< 0.1%
0.9950956 1
< 0.1%
0.9932274 1
< 0.1%
0.99279433 1
< 0.1%
0.99267393 1
< 0.1%
0.992002 1
< 0.1%
0.9901102 1
< 0.1%
0.9889313 1
< 0.1%
0.9884332 1
< 0.1%
0.9871696 1
< 0.1%

label_1
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size456.3 KiB
0
49578 
1
8808 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters58386
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 49578
84.9%
1 8808
 
15.1%

Length

2024-08-16T16:19:29.210147image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-16T16:19:29.306545image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 49578
84.9%
1 8808
 
15.1%

Most occurring characters

ValueCountFrequency (%)
0 49578
84.9%
1 8808
 
15.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58386
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 49578
84.9%
1 8808
 
15.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58386
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 49578
84.9%
1 8808
 
15.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58386
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 49578
84.9%
1 8808
 
15.1%

pred_2
Real number (ℝ)

HIGH CORRELATION 

Distinct58258
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02329472
Minimum3.1557033 × 10-6
Maximum0.74713737
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.3 KiB
2024-08-16T16:19:29.417662image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum3.1557033 × 10-6
5-th percentile0.00015165282
Q10.001014794
median0.0043468919
Q30.019601202
95-th percentile0.11696669
Maximum0.74713737
Range0.74713421
Interquartile range (IQR)0.018586408

Descriptive statistics

Standard deviation0.051255015
Coefficient of variation (CV)2.2002847
Kurtosis27.029555
Mean0.02329472
Median Absolute Deviation (MAD)0.0039987507
Skewness4.4771904
Sum1360.0855
Variance0.0026270766
MonotonicityNot monotonic
2024-08-16T16:19:29.562208image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.011892402 2
 
< 0.1%
0.018520292 2
 
< 0.1%
0.01694852 2
 
< 0.1%
0.005958382 2
 
< 0.1%
0.0034950266 2
 
< 0.1%
0.0023882305 2
 
< 0.1%
0.00014194647 2
 
< 0.1%
0.011995183 2
 
< 0.1%
0.0008231159 2
 
< 0.1%
0.00318263 2
 
< 0.1%
Other values (58248) 58366
> 99.9%
ValueCountFrequency (%)
3.1557033 × 10-61
< 0.1%
3.6686322 × 10-61
< 0.1%
4.1523763 × 10-61
< 0.1%
4.6425284 × 10-61
< 0.1%
5.1734055 × 10-61
< 0.1%
5.183856 × 10-61
< 0.1%
5.324474 × 10-61
< 0.1%
5.3583226 × 10-61
< 0.1%
5.3898316 × 10-61
< 0.1%
5.4543116 × 10-61
< 0.1%
ValueCountFrequency (%)
0.74713737 1
< 0.1%
0.68075085 1
< 0.1%
0.63492453 1
< 0.1%
0.62049097 1
< 0.1%
0.61003417 1
< 0.1%
0.60766214 1
< 0.1%
0.60516906 1
< 0.1%
0.5874526 1
< 0.1%
0.58547086 1
< 0.1%
0.58512235 1
< 0.1%

label_2
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size456.3 KiB
0
53717 
1
 
4669

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters58386
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 53717
92.0%
1 4669
 
8.0%

Length

2024-08-16T16:19:29.696010image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-16T16:19:29.789973image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 53717
92.0%
1 4669
 
8.0%

Most occurring characters

ValueCountFrequency (%)
0 53717
92.0%
1 4669
 
8.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58386
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 53717
92.0%
1 4669
 
8.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58386
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 53717
92.0%
1 4669
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58386
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 53717
92.0%
1 4669
 
8.0%

pred_3
Real number (ℝ)

Distinct58084
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0077101552
Minimum0.00012598517
Maximum0.19338268
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.3 KiB
2024-08-16T16:19:29.907327image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0.00012598517
5-th percentile0.0015437935
Q10.0034136534
median0.005793022
Q30.0096697836
95-th percentile0.019977439
Maximum0.19338268
Range0.19325669
Interquartile range (IQR)0.0062561303

Descriptive statistics

Standard deviation0.0071315162
Coefficient of variation (CV)0.92495104
Kurtosis41.921935
Mean0.0077101552
Median Absolute Deviation (MAD)0.0028272582
Skewness4.2254181
Sum450.16512
Variance5.0858523 × 10-5
MonotonicityNot monotonic
2024-08-16T16:19:30.065835image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.007883686 3
 
< 0.1%
0.009535172 2
 
< 0.1%
0.0020297398 2
 
< 0.1%
0.005581074 2
 
< 0.1%
0.011066267 2
 
< 0.1%
0.0074752197 2
 
< 0.1%
0.0048888237 2
 
< 0.1%
0.008028185 2
 
< 0.1%
0.0037553138 2
 
< 0.1%
0.013138089 2
 
< 0.1%
Other values (58074) 58365
> 99.9%
ValueCountFrequency (%)
0.00012598517 1
< 0.1%
0.00013584696 1
< 0.1%
0.0001408542 1
< 0.1%
0.00015250737 1
< 0.1%
0.00015432482 1
< 0.1%
0.0001975599 1
< 0.1%
0.00019824866 1
< 0.1%
0.00021555979 1
< 0.1%
0.00022349285 1
< 0.1%
0.00023816832 1
< 0.1%
ValueCountFrequency (%)
0.19338268 1
< 0.1%
0.17355661 1
< 0.1%
0.137751 1
< 0.1%
0.13011244 1
< 0.1%
0.12925966 1
< 0.1%
0.1275461 1
< 0.1%
0.11823893 1
< 0.1%
0.111909024 1
< 0.1%
0.11189618 1
< 0.1%
0.11088168 1
< 0.1%

label_3
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size456.3 KiB
0
57202 
1
 
1184

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters58386
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 57202
98.0%
1 1184
 
2.0%

Length

2024-08-16T16:19:30.210707image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-16T16:19:30.305690image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 57202
98.0%
1 1184
 
2.0%

Most occurring characters

ValueCountFrequency (%)
0 57202
98.0%
1 1184
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58386
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 57202
98.0%
1 1184
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58386
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 57202
98.0%
1 1184
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58386
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 57202
98.0%
1 1184
 
2.0%

pred_4
Real number (ℝ)

HIGH CORRELATION 

Distinct58263
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2211176
Minimum0.0038607
Maximum0.93400216
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.3 KiB
2024-08-16T16:19:30.421683image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0.0038607
5-th percentile0.040009527
Q10.098113671
median0.18470399
Q30.31074162
95-th percentile0.52296796
Maximum0.93400216
Range0.93014146
Interquartile range (IQR)0.21262795

Descriptive statistics

Standard deviation0.1541044
Coefficient of variation (CV)0.69693411
Kurtosis0.68908138
Mean0.2211176
Median Absolute Deviation (MAD)0.099361412
Skewness1.0124967
Sum12910.172
Variance0.023748166
MonotonicityNot monotonic
2024-08-16T16:19:30.560000image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.33021012 2
 
< 0.1%
0.12056588 2
 
< 0.1%
0.27612534 2
 
< 0.1%
0.22770756 2
 
< 0.1%
0.22043061 2
 
< 0.1%
0.13232286 2
 
< 0.1%
0.056873277 2
 
< 0.1%
0.06956278 2
 
< 0.1%
0.16752589 2
 
< 0.1%
0.20707266 2
 
< 0.1%
Other values (58253) 58366
> 99.9%
ValueCountFrequency (%)
0.0038607 1
< 0.1%
0.0063759116 1
< 0.1%
0.006380804 1
< 0.1%
0.006406149 1
< 0.1%
0.0067538884 1
< 0.1%
0.0068781367 1
< 0.1%
0.007397928 1
< 0.1%
0.007492071 1
< 0.1%
0.007721727 1
< 0.1%
0.007755888 1
< 0.1%
ValueCountFrequency (%)
0.93400216 1
< 0.1%
0.9310748 1
< 0.1%
0.9279111 1
< 0.1%
0.9268072 1
< 0.1%
0.919865 1
< 0.1%
0.91918445 1
< 0.1%
0.9060331 1
< 0.1%
0.90261084 1
< 0.1%
0.90051305 1
< 0.1%
0.89736754 1
< 0.1%

label_4
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size456.3 KiB
0
44761 
1
13625 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters58386
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 44761
76.7%
1 13625
 
23.3%

Length

2024-08-16T16:19:30.694221image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-16T16:19:30.797703image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 44761
76.7%
1 13625
 
23.3%

Most occurring characters

ValueCountFrequency (%)
0 44761
76.7%
1 13625
 
23.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58386
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 44761
76.7%
1 13625
 
23.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58386
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 44761
76.7%
1 13625
 
23.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58386
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 44761
76.7%
1 13625
 
23.3%

pred_5
Real number (ℝ)

HIGH CORRELATION 

Distinct58251
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.48817325
Minimum0.002602684
Maximum0.99169874
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.3 KiB
2024-08-16T16:19:30.918188image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0.002602684
5-th percentile0.061654868
Q10.21790319
median0.48550943
Q30.75545749
95-th percentile0.92399838
Maximum0.99169874
Range0.98909606
Interquartile range (IQR)0.5375543

Descriptive statistics

Standard deviation0.29015098
Coefficient of variation (CV)0.59436068
Kurtosis-1.3465462
Mean0.48817325
Median Absolute Deviation (MAD)0.2688394
Skewness0.020961991
Sum28502.483
Variance0.084187594
MonotonicityNot monotonic
2024-08-16T16:19:31.074483image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.44168693 3
 
< 0.1%
0.517416 2
 
< 0.1%
0.5902283 2
 
< 0.1%
0.4765694 2
 
< 0.1%
0.76737803 2
 
< 0.1%
0.82156795 2
 
< 0.1%
0.86065304 2
 
< 0.1%
0.36439192 2
 
< 0.1%
0.46140862 2
 
< 0.1%
0.14729002 2
 
< 0.1%
Other values (58241) 58365
> 99.9%
ValueCountFrequency (%)
0.002602684 1
< 0.1%
0.0026368657 1
< 0.1%
0.0027910196 1
< 0.1%
0.0035510373 1
< 0.1%
0.0037912827 1
< 0.1%
0.0043345774 1
< 0.1%
0.004630072 1
< 0.1%
0.005466228 1
< 0.1%
0.005474602 1
< 0.1%
0.005476258 1
< 0.1%
ValueCountFrequency (%)
0.99169874 1
< 0.1%
0.99030745 1
< 0.1%
0.9897059 1
< 0.1%
0.98965704 1
< 0.1%
0.9894952 1
< 0.1%
0.989023 1
< 0.1%
0.98899287 1
< 0.1%
0.98837215 1
< 0.1%
0.9880542 1
< 0.1%
0.98787886 1
< 0.1%

label_5
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size456.3 KiB
0
32418 
1
25968 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters58386
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 32418
55.5%
1 25968
44.5%

Length

2024-08-16T16:19:31.224245image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-16T16:19:31.322022image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 32418
55.5%
1 25968
44.5%

Most occurring characters

ValueCountFrequency (%)
0 32418
55.5%
1 25968
44.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58386
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 32418
55.5%
1 25968
44.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58386
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 32418
55.5%
1 25968
44.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58386
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 32418
55.5%
1 25968
44.5%

Interactions

2024-08-16T16:19:25.667611image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:18.760334image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:19.685314image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:20.567163image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:21.443591image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:22.825867image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:23.750728image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:24.744080image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:25.777102image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:18.876997image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:19.794605image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:20.678444image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:21.556696image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:22.949983image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:23.871691image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:24.852000image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:25.881267image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:18.983269image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:19.897309image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:20.783322image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:21.666081image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:23.058346image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:23.979699image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:24.957877image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:25.984020image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:19.098210image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:19.998516image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:20.892795image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:21.772520image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:23.174977image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:24.091438image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:25.130731image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:26.089958image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:19.213372image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:20.110069image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:20.996088image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:21.878018image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:23.286714image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:24.206251image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:25.236513image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:26.204099image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:19.331791image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:20.226141image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:21.111439image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:22.485843image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:23.404349image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:24.395458image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:25.348082image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:26.322849image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:19.462987image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:20.349129image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:21.232218image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:22.607497image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:23.529784image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:24.521408image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:25.463616image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:26.422148image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:19.573215image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:20.460300image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:21.334672image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:22.716139image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:23.637064image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:24.630760image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-16T16:19:25.560213image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Correlations

2024-08-16T16:19:31.404980image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
agegenderlabel_0label_1label_2label_3label_4label_5pred_0pred_1pred_2pred_3pred_4pred_5racesubject_id
age1.0000.0800.1500.1620.1420.0250.1240.2640.3760.3960.3740.2290.364-0.4430.1510.009
gender0.0801.0000.0090.0250.0000.0110.0450.0560.0790.0410.0090.0440.0770.0740.1550.033
label_00.1500.0091.0000.1670.1990.0070.0500.3400.3160.2050.1670.0510.1990.2650.0650.026
label_10.1620.0250.1671.0000.2380.0090.1600.3770.2930.5410.2260.0670.3720.4720.1210.023
label_20.1420.0000.1990.2381.0000.0160.0920.2640.2910.2780.3710.0160.2850.3280.0660.031
label_30.0250.0110.0070.0090.0161.0000.0150.1290.0100.0220.0000.0410.0260.0310.0510.018
label_40.1240.0450.0500.1600.0920.0151.0000.4940.1840.2430.1470.0210.3120.2980.0810.024
label_50.2640.0560.3400.3770.2640.1290.4941.0000.3850.4240.2130.0860.4700.5270.1290.028
pred_00.3760.0790.3160.2930.2910.0100.1840.3851.0000.6470.7700.2230.581-0.7440.0390.005
pred_10.3960.0410.2050.5410.2780.0220.2430.4240.6471.0000.7160.2650.790-0.9030.0560.011
pred_20.3740.0090.1670.2260.3710.0000.1470.2130.7700.7161.0000.2420.707-0.7760.0220.016
pred_30.2290.0440.0510.0670.0160.0410.0210.0860.2230.2650.2421.0000.211-0.3230.0200.011
pred_40.3640.0770.1990.3720.2850.0260.3120.4700.5810.7900.7070.2111.000-0.8830.0520.012
pred_5-0.4430.0740.2650.4720.3280.0310.2980.527-0.744-0.903-0.776-0.323-0.8831.0000.065-0.014
race0.1510.1550.0650.1210.0660.0510.0810.1290.0390.0560.0220.0200.0520.0651.0000.096
subject_id0.0090.0330.0260.0230.0310.0180.0240.0280.0050.0110.0160.0110.012-0.0140.0961.000

Missing values

2024-08-16T16:19:26.565389image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-16T16:19:26.848772image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-08-16T16:19:27.119135image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

subject_idageracegenderpred_0label_0pred_1label_1pred_2label_2pred_3label_3pred_4label_4pred_5label_5
01181275268WHITEF0.05614300.01501200.00117500.00426900.09128500.7594781
11181275268WHITEF0.05106000.00602800.00179900.00651500.05038700.8638491
21181275268WHITEF0.02137200.00405900.00074300.00447500.09296700.8495081
31181275268WHITEF0.03184200.00186200.00079700.00619600.04719100.9348081
415197921255NaNNaN0.03459800.01069700.00050300.00524300.07757500.8717801
515197921255NaNNaN0.03293600.00187700.00049000.00138200.07746400.9053161
61526476689BLACK/AFRICAN AMERICANF0.67189610.49377610.26708910.00497100.28281300.1031940
71526476689BLACK/AFRICAN AMERICANF0.81410910.50285210.46141610.01126400.31068200.0671070
81526476689BLACK/AFRICAN AMERICANF0.30345400.52176810.10727500.00421800.49292810.1113240
91526476689BLACK/AFRICAN AMERICANF0.36186900.13794010.01442600.00308400.21234110.3432580
subject_idageracegenderpred_0label_0pred_1label_1pred_2label_2pred_3label_3pred_4label_4pred_5label_5
583761779054259UNKNOWNF0.07497600.00491300.00039500.00137200.06132300.8916541
583771779054259UNKNOWNF0.18251100.01312400.00461300.00297400.09101500.6902881
5837810271316255NaNNaN0.55166900.58329400.21270000.00174600.58820600.0308870
5837910271316255NaNNaN0.23650700.56754000.04217000.00420100.27306200.1552880
583801118315469WHITEM0.06815600.01927600.00047100.00828400.11042400.7529961
583811118315469WHITEM0.10489000.01157600.00118900.00339000.05721800.8516561
583821673662646WHITEM0.09921600.07140500.00449200.00172200.20077700.6132871
583831673662646WHITEM0.43983400.02378600.00085800.00712000.11207700.5493231
583841673662646WHITEM0.44696700.28439000.00941200.02279100.29475900.1503140
583851673662646WHITEM0.43007900.48422100.05230110.01131200.24324000.1586330

Duplicate rows

Most frequently occurring

subject_idageracegenderpred_0label_0pred_1label_1pred_2label_2pred_3label_3pred_4label_4pred_5label_5# duplicates
01340909354WHITEF0.07488200.65634910.00036500.00109300.18641410.24669202
11340909354WHITEF0.10361600.22209910.00261000.00489100.29773510.31292802
21340909354WHITEF0.20190500.65100710.02019510.00242500.39855000.16569002
31340909354WHITEF0.24077200.34766610.02854510.00471900.55279400.21958102
41340909354WHITEF0.26383400.15399710.00318300.01225500.56360800.14729002
51340909354WHITEF0.29160500.05840610.00238200.00092100.31676100.31883602
61389236918BLACK/AFRICAN AMERICANM0.03040200.02249800.00054800.00600800.10910100.81690612
71389236918BLACK/AFRICAN AMERICANM0.06824400.00719800.00070600.00247900.10834900.87285212
81424248852NaNF0.31888300.44913100.00119300.00798900.23481100.28203512
91424248852NaNF0.37791900.19919100.00724500.00316600.38595400.24055412